# YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection

arXiv: https://arxiv.org/abs/2103.09422 · HTML: https://mlsanity.com/paper/2103.09422/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 35 references that are in the map.
- First posted on arXiv 17 Mar 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [GhostNet: More Features from Cheap Operations](https://mlsanity.com/paper/1911.11907/) (2019)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [M3D-RPN: Monocular 3D Region Proposal Network for Object Detection](https://mlsanity.com/paper/1907.06038/) (2019)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)
- [Learning Depth-Guided Convolutions for Monocular 3D Object Detection](https://mlsanity.com/paper/1912.04799/) (2019)
- [Stereo R-CNN based 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09738/) (2019)
- [MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships](https://mlsanity.com/paper/2003.00504/) (2020)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving](https://mlsanity.com/paper/2001.03343/) (2020)
- [Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://mlsanity.com/paper/1903.11444/) (2019)
- [Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud](https://mlsanity.com/paper/1903.09847/) (2019)
- [DSGN: Deep Stereo Geometry Network for 3D Object Detection](https://mlsanity.com/paper/2001.03398/) (2020)
- [How do neural networks see depth in single images?](https://mlsanity.com/paper/1905.07005/) (2019)
- [End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection](https://mlsanity.com/paper/2004.03080/) (2020)
- [HDNET: Exploiting HD Maps for 3D Object Detection](https://mlsanity.com/paper/2012.11704/) (2020)
- [Ground-aware Monocular 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/2102.00690/) (2021)
- [Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching](https://mlsanity.com/paper/1909.03751/) (2019)
- [Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation](https://mlsanity.com/paper/2004.03572/) (2020)
- [FADNet: A Fast and Accurate Network for Disparity Estimation](https://mlsanity.com/paper/2003.10758/) (2020)
- [Object-Centric Stereo Matching for 3D Object Detection](https://mlsanity.com/paper/1909.07566/) (2019)
- [ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection](https://mlsanity.com/paper/2003.00529/) (2020)
- [RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1911.09712/) (2019)
- [Focal Loss in 3D Object Detection](https://mlsanity.com/paper/1809.06065/) (2018)

## Built on by (most cited first, 30 of 34)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [3D Object Detection for Autonomous Driving: A Survey](https://mlsanity.com/paper/2106.10823/) (2021)
- [AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection](https://mlsanity.com/paper/2108.11127/) (2021)
- [Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.02112/) (2022)
- [Object Detection with Transformers: A Review](https://mlsanity.com/paper/2306.04670/) (2023)
- [MonoGAE: Roadside Monocular 3D Object Detection with Ground-Aware Embeddings](https://mlsanity.com/paper/2310.00400/) (2023)
- [SGM3D: Stereo Guided Monocular 3D Object Detection](https://mlsanity.com/paper/2112.01914/) (2021)
- [Vision-based Learning for Drones: A Survey](https://mlsanity.com/paper/2312.05019/) (2023)
- [DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors](https://mlsanity.com/paper/2204.03039/) (2022)
- [SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection](https://mlsanity.com/paper/2305.07270/) (2023)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning](https://mlsanity.com/paper/2306.01980/) (2023)
- [ORA3D: Overlap Region Aware Multi-view 3D Object Detection](https://mlsanity.com/paper/2207.00865/) (2022)
- [ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection](https://mlsanity.com/paper/2111.14055/) (2021)
- [StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection](https://mlsanity.com/paper/2301.01615/) (2023)
- [Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training](https://mlsanity.com/paper/2310.00920/) (2023)
- [BEVStereo++: Accurate Depth Estimation in Multi-view 3D Object Detection via Dynamic Temporal Stereo](https://mlsanity.com/paper/2304.04185/) (2023)
- [Unleashing the Temporal Potential of Stereo Event Cameras for Continuous-Time 3D Object Detection](https://mlsanity.com/paper/2508.02288/) (2025)
- [FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection](https://mlsanity.com/paper/2209.07419/) (2022)
- [You Only Look Bottom-Up for Monocular 3D Object Detection](https://mlsanity.com/paper/2401.15319/) (2024)
- [Transformer-based stereo-aware 3D object detection from binocular images](https://mlsanity.com/paper/2304.11906/) (2023)
- [Stereo-based 3D Anomaly Object Detection for Autonomous Driving: A New Dataset and Baseline](https://mlsanity.com/paper/2507.09214/) (2025)
- [CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction](https://mlsanity.com/paper/2508.12917/) (2025)
- [StereoDETR: Stereo-based Transformer for 3D Object Detection](https://mlsanity.com/paper/2511.18788/) (2025)
- [Joint stereo 3D object detection and implicit surface reconstruction](https://mlsanity.com/paper/2111.12924/) (2021)
- [MonoNext: A 3D Monocular Object Detection with ConvNext](https://mlsanity.com/paper/2308.00596/) (2023)
- [Real-time Stereo-based 3D Object Detection for Streaming Perception](https://mlsanity.com/paper/2410.12394/) (2024)
- [DDStereo: Efficient Dual Decoder Transformers for Stereo 3D Road Anomaly Detection](https://mlsanity.com/paper/2606.24805/) (2026)
- [Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection](https://mlsanity.com/paper/2106.10013/) (2021)
- [MDS-Net: A Multi-scale Depth Stratification Based Monocular 3D Object Detection Algorithm](https://mlsanity.com/paper/2201.04341/) (2022)

## Abstract (arXiv)

Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D image-based detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D.